where \(n\)  is the number of resolution scales and \(\gamma ^{i} \in \mathbb {R}^+\)  is the loss weight at scale \(i\) , \(\mathcal {L}_{log}\)  is a variation of the \(L_1\)  norm that minimizes error on the sparse depth pixels, \(\mathcal {L}_{grad}\)  optimizes the error on edge structures, and \(\mathcal {L}_{norm}\)  penalizes angular error between the ground truth and predicted normal surfaces. These loss terms were introduced by Hu et al.  [1]} and widely adopted by state-of-art monocular depth estimation methods [2]}, [3]}. Subsection REF  describes in detail how the network is trained using these loss functions.
